Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

Current Advances and Outlook in Gastric Cancer Chemoresistance: A Review

Background: Surgical resection of the lesion is the standard primary treatment of gastric cancer. Unfortunately, most patients are already in the advanced stage of the disease when they are diagnosed with gastric cancer. Alternative therapies, such as radiation therapy and chemotherapy, can achieve only very limited benefits. The emergence of cancer drug resistance has always been the major obstacle to the cure of tumors. The main goal of modern cancer pharmacology is to determine the underlying mechanism of anticancer drugs. Objective: Here, we mainly review the latest research results related to the mechanism of chemotherapy resistance in gastric cancer, the application of natural products in overcoming the chemotherapy resistance of gastric cancer, and the new strategies currently being developed to treat tumors based on immunotherapy and gene therapy. Conclusion: The emergence of cancer drug resistance is the main obstacle in achieving alleviation and final cure for gastric cancer. Mixed therapies are considered to be a possible way to overcome chemoresistance. Natural products are the main resource for discovering new drugs specific for treating chemoresistance, and further research is needed to clarify the mechanism of natural product activity in patients.

Acquired ABC-transporter overexpression in cancer cells: transcriptional induction or Darwinian selection?

Acquired multidrug resistance (MDR) in tumor diseases has repeatedly been associated with overexpression of ATP-binding cassette transporters (ABC-transporters) such as P-glycoprotein. Both in vitro and in vivo data suggest that these efflux transporters can cause MDR, albeit its actual relevance for clinical chemotherapy unresponsiveness remains uncertain. The overexpression can experimentally be achieved by exposure of tumor cells to cytotoxic drugs. For simplification, the drug-mediated transporter overexpression can be attributed to two opposite mechanisms: First, increased transcription of ABC-transporter genes mediated by nuclear receptors sensing the respective compound. Second, Darwinian selection of sub-clones intrinsically overexpressing drug transporters being capable of extruding the respective drug. To date, there is no definite data indicating which mechanism truly applies or whether there are circumstances promoting either mode of action. This review summarizes experimental evidence for both theories, suggests an algorithm discriminating between these two modes, and finally points out future experimental approaches of research to answer this basic question in cancer pharmacology.

Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro

In cancer pharmacology, a drug candidate’s therapeutic potential is typically expressed as its ability to suppress cell growth. Different methods in assessing the cell phenotype and calculating the drug effect have been established. However, inconsistencies in drug response outcomes have been reported, and it is still unclear whether and to what extent the choice of data post-processing methods is responsible for that. Studies that systematically examine these questions are rare. Here, we compare three established calculation methods on a collection of nine in vitro models of glioblastoma, exposed to a library of 231 clinical drugs. The therapeutic potential of the drugs is determined on the growth curves, using growth inhibition 50% (GI50) and point-of-departure (PoD) as the criteria. An effect is detected on 36% of the drugs when relying on GI50 and on 27% when using PoD. For the area under the curve (AUC), a threshold of 9.5 or 10 could be set to discriminate between the drugs with and without an effect. GI50, PoD, and AUC are highly correlated. The ranking of substances by different criteria varies somewhat, but the group of the top 20 substances according to one criterion typically includes 17–19 top candidates according to another. In addition to generating preclinical values with high clinical potential, we present off-target appreciation of top substance predictions by interrogating the drug response data of non-cancer cells in our calculation technology.

Novel Mitochondria-targeted Drugs for Cancer Therapy

: The search for mitochondria-targeted drugs has dramatically risen over the last decade. Mitochondria are essential organelles serving not only as a powerhouse of the cell but also as a key player in cell proliferation and cell death. Their central role in the energetic metabolism, calcium homeostasis and apoptosis makes them an intriguing field of interest for cancer pharmacology. In cancer cells, many mitochondrial signaling and metabolic pathways are altered. These changes contribute to cancer development and progression. Due to changes in the mitochondrial metabolism and changes in a membrane potential, cancer cells are more susceptible to mitochondria-targeted therapy and the loss of functional mitochondria leads to arrest of cancer progression and/or a cancer cell death. Identification of mitochondrial changes specific for tumor growth and progression, a rational development of new mitochondria-targeted drugs and research on delivery agents led to the advance of this promising area. This review will highlight the current findings in mitochondrial biology which are important for cancer initiation, progression and resistance and discuss approaches of cancer pharmacology with a special focus to the anti-cancer drugs referred to as ‘mitocans’.

A living biobank of matched pairs of patient-derived xenografts and organoids for cancer pharmacology

Patient-derived tumor xenograft (PDX)/organoid (PDO), driven by cancer stem cells (CSC), are considered the most predictive models for translational oncology. Large PDX collections reflective of patient populations have been created and used extensively to test various investigational therapies, including in population-trials as surrogate subjects in vivo. PDOs are recognized as in vitro surrogates for patients amenable for high-throughput screening (HTS). We have built a biobank of carcinoma PDX-derived organoids (PDXOs) by converting an existing PDX library and confirmed high degree of similarities between PDXOs and parental PDXs in genomics, histopathology and pharmacology, suggesting “biological equivalence or interchangeability” between the two. Here we demonstrate the applications of PDXO biobank for HTS “matrix” screening for both lead compounds and indications, immune cell co-cultures for immune-therapies and engineering enables in vitro/in vivo imaging. This large biobank of matched pairs of PDXs/PDXOs across different cancers could become powerful tools for the future cancer drug discovery.

Pharmacologic Management for Cancer Pain

Chronic pain is a major factor that impairs functionality and compromises quality of life. Unfortunately, this type of pain is often under-treated due to lack of education about the use and range of effective non-pharmacologic modalities, about how to use and monitor pharmacologic modalities, and the fear about real and imagined adverse effects that are associated with its management. Cancer is diagnosed in approximately 40% of the population with up to 89% experiencing some form of pain in the later stages of the disease. Unfortunately, a significant portion of this population receives inadequate treatment for their pain. Here we provide a review of the evidence-base for determining the best approach for managing the pain of malignancy in the hope of providing a basic framework for the physician to better utilize the pharmacological options that comprise an important component of comprehensive pain care for the cancer patient. This review contains 2 figures, 7 tables, and 106 references. Keywords: Cancer pain, malignant pain, opioids, chronic pain, analgesic options, adjuvant options, cancer pharmacology, evidence-based pain treatment

Pharmacologic Management for Cancer Pain

Chronic pain is a major factor that impairs functionality and compromises quality of life. Unfortunately, this type of pain is often under-treated due to lack of education about the use and range of effective non-pharmacologic modalities, about how to use and monitor pharmacologic modalities, and the fear about real and imagined adverse effects that are associated with its management. Cancer is diagnosed in approximately 40% of the population with up to 89% experiencing some form of pain in the later stages of the disease. Unfortunately, a significant portion of this population receives inadequate treatment for their pain. Here we provide a review of the evidence-base for determining the best approach for managing the pain of malignancy in the hope of providing a basic framework for the physician to better utilize the pharmacological options that comprise an important component of comprehensive pain care for the cancer patient. This review contains 2 figures, 7 tables, and 106 references. Keywords: Cancer pain, malignant pain, opioids, chronic pain, analgesic options, adjuvant options, cancer pharmacology, evidence-based pain treatment